mlsanityData to 7 Oct 2026

Contextualized Automatic Speech Recognition with Attention-Based Bias Phrase Boosted Beam Search

arXiv 2401.10449 ↗First posted 19 Jan 2024eess.AS cs.CL cs.SDcited by 19

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Abstract (arXiv)

End-to-end (E2E) automatic speech recognition (ASR) methods exhibit remarkable performance. However, since the performance of such methods is intrinsically linked to the context present in the training data, E2E-ASR methods do not perform as desired for unseen user contexts (e.g., technical terms, personal names, and playlists). Thus, E2E-ASR methods must be easily contextualized by the user or developer. This paper proposes an attention-based contextual biasing method that can be customized using an editable phrase list (referred to as a bias list). The proposed method can be trained effectively by combining a bias phrase index loss and special tokens to detect the bias phrases in the input speech data. In addition, to improve the contextualization performance during inference further, we propose a bias phrase boosted (BPB) beam search algorithm based on the bias phrase index probability. Experimental results demonstrate that the proposed method consistently improves the word error rate and the character error rate of the target phrases in the bias list on both the Librispeech-960 (English) and our in-house (Japanese) dataset, respectively.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). Builds on 21 of its 31 references that are in the map. First posted on arXiv 19 Jan 2024.

Builds on

21 of 31 references.

References · 21

  1. 2020Conformer: Convolution-augmented Transformer for Speech Recognitioncited by 1,838
  2. 2019SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognitioncited by 1,355
  3. 2018ESPnet: End-to-End Speech Processing Toolkitcited by 554
  4. 2015Listen, Attend and Spellcited by 542
  5. 2015Attention-Based Models for Speech Recognitioncited by 400
  6. 2021Recent Advances in End-to-End Automatic Speech Recognitioncited by 138
  7. 2018Deep context: end-to-end contextual speech recognitioncited by 136
  8. 2023End-to-End Speech Recognition: A Surveycited by 127
  9. 2017An analysis of incorporating an external language model into a sequence-to-sequence modelcited by 109
  10. 2021Contextualized Streaming End-to-End Speech Recognition with Trie-Based Deep Biasing and Shallow Fusioncited by 100
  11. 2017Cold Fusion: Training Seq2Seq Models Together with Language Modelscited by 87
  12. 2020Contextual RNN-T For Open Domain ASRcited by 85
9 more
  1. 2019Two-Pass End-to-End Speech Recognitioncited by 62
  2. 2020Class LM and word mapping for contextual biasing in End-to-End ASRcited by 42
  3. 2022Improving End-to-End Contextual Speech Recognition with Fine-Grained Contextual Knowledge Selectioncited by 39
  4. 2023Contextualized End-to-End Speech Recognition with Contextual Phrase Prediction Networkcited by 31
  5. 2021Instant One-Shot Word-Learning for Context-Specific Neural Sequence-to-Sequence Speech Recognitioncited by 30
  6. 2022Towards Contextual Spelling Correction for Customization of End-to-end Speech Recognition Systemscited by 17
  7. 2023CopyNE: Better Contextual ASR by Copying Named Entitiescited by 13
  8. 20224D ASR: Joint modeling of CTC, Attention, Transducer, and Mask-Predict decoderscited by 7
  9. 2023Retraining-free Customized ASR for Enharmonic Words Based on a Named-Entity-Aware Model and Phoneme Similarity Estimationcited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 5; 2025: 9; 2026: 5.

19 papers.

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  1. 2024CTC-Assisted LLM-Based Contextual ASRcited by 18
  2. 2024Contextualized End-to-end Automatic Speech Recognition with Intermediate Biasing Losscited by 9
  3. 2024Contextualized Automatic Speech Recognition with Dynamic Vocabularycited by 8
  4. 2026SLAM-LLM: A Modular, Open-Source Multimodal Large Language Model Framework and Best Practice for Speech, Language, Audio and Music Processingcited by 7
  5. 2024InterBiasing: Boost Unseen Word Recognition through Biasing Intermediate Predictionscited by 5
  6. 2025OWSM-Biasing: Contextualizing Open Whisper-Style Speech Models for Automatic Speech Recognition with Dynamic Vocabularycited by 4
  7. 2024Optimizing Contextual Speech Recognition Using Vector Quantization for Efficient Retrievalcited by 3
  8. 2025WCTC-Biasing: Retraining-free Contextual Biasing ASR with Wildcard CTC-based Keyword Spotting and Inter-layer Biasingcited by 3
  9. 2025Zero-shot Context Biasing with Trie-based Decoding using Synthetic Multi-Pronunciationcited by 3
  10. 2025DYNAC: Dynamic Vocabulary based Non-Autoregressive Contextualization for Speech Recognitioncited by 1
  11. 2025PMF-CEC: Phoneme-augmented Multimodal Fusion for Context-aware ASR Error Correction with Error-specific Selective Decodingcited by 1
  12. 2025Enhancing the Robustness of Contextual ASR to Varying Biasing Information Volumes Through Purified Semantic Correlation Joint Modelingcited by 1
7 more
  1. 2025A Neural Model for Contextual Biasing Score Learning and Filteringcited by 1
  2. 2026How to Recognize New Words: A Comparison Between Context Biasing Methods and Speech LLMscited by 1
  3. 2025Context Biasing for Pronunciation-Orthography Mismatch in Automatic Speech Recognitioncited by 0
  4. 2025PARCO: Phoneme-Augmented Robust Contextual ASR via Contrastive Entity Disambiguationcited by 0
  5. 2026CALM: Joint Contextual Acoustic-Linguistic Modeling for Personalization of Multi-Speaker ASRcited by 0
  6. 2026JSPG: Dynamic Dictionary Filtering via Joint Semantic-Pinyin-Glyph Retrieval for Chinese Contextual ASRcited by 0
  7. 2026COALA: Robust Contextualized Speech-augmented Language Modeling for ASR via Contrastive Regularizer and Biasing Score Estimationcited by 0